IMDB Analysis of IMDB TV Shows from 1990–2018, Their Ratings and Viewer Trends

Author

Noah & William

# Abstract

This project explores IMDb ratings and viewer trends to better understand how audience preferences and genre popularity have evolved over time. By analyzing historical data across multiple genres, we aim to identify the most influential patterns shaping TV reception. Through data-driven visualizations and trend analysis, this study uncovers meaningful insights into shifting viewer behaviors, highlighting how cultural, temporal, and genre-based factors contribute to changes in film popularity and audience trends.

Top 5 Show Seasons From 1990-2018

touched by an angel Santa Barbara L.A Law Game of Thrones Fugitive Chronicles

                       title  av_rating  genres  year
800      Touched by an Angel   9.600000   Drama  1998
576            Santa Barbara   9.400000   Drama  1990
379                 L.A. Law   9.350000   Drama  1990
270          Game of Thrones   9.265114  Action  2011
696  The Fugitive Chronicles   9.200000   Crime  2010

Best Performing Genres From 1990-2018

The following analysis identifies the best-rated TV genre for each year based on average IMDb ratings.
To achieve this, the dataset was first expanded so that shows belonging to multiple genres were counted under each individual genre.
The average rating (av_rating) was then calculated for each combination of year and genre.
Finally, for every year, the single genre with the highest average rating was selected, resulting in a clean timeline of the top-performing genres across all years.

The resulting figure (see @genres_table) visualizes how the top genre changes over time, highlighting shifting audience preferences and trends in television content.
For example, [insert your observation here — e.g., “Drama dominates the early 2010s, while Documentary becomes more prominent after 2018.”]
This view provides a concise overview of how viewer tastes evolved according to IMDb data.

year genres av_rating
0 1990 Romance 8.650000
1 1991 Fantasy 8.703800
2 1992 Fantasy 8.601900
3 1993 Fantasy 9.204300
4 1994 Family 8.687825
5 1995 Mystery 8.505175
6 1996 Animation 8.331350
7 1997 Thriller 8.619500
8 1998 Family 8.783133
9 1999 Thriller 8.539300
10 2000 Thriller 8.613200
11 2001 History 8.684800
12 2002 Animation 8.298450
13 2003 Thriller 8.228680
14 2004 Sport 8.746600
15 2005 Sport 8.719800
16 2006 Sport 9.050200
17 2007 Horror 8.485000
18 2008 Sport 8.683767
19 2009 Sport 8.442967
20 2010 Horror 8.752800
21 2011 Sport 8.611500
22 2012 Fantasy 8.397514
23 2013 Sport 8.542500
24 2014 War 8.453800
25 2015 Family 8.565500
26 2016 War 9.202200
27 2017 War 9.131400
28 2018 Animation 9.473800

Mathematical Representation of Top Genre by Year

Let the following definitions hold:

\[ G = \{ g_1, g_2, \dots, g_m \} \quad \text{(set of all movie genres)} \]

\[ Y = \{ y_1, y_2, \dots, y_n \} \quad \text{(set of all years)} \]

\[ R_{i,j,k} \text{ — IMDb rating of the } k\text{th movie released in year } y_i \text{ belonging to genre } g_j \]

\[ N_{i,j} \text{ — number of movies of genre } g_j \text{ in year } y_i \]

The average rating for genre ( g_j ) in year ( y_i ) is:

\[ \bar{R}(y_i, g_j) = \frac{1}{N_{i,j}} \sum_{k=1}^{N_{i,j}} R_{i,j,k} \]

The top genre for each year is the one with the highest average rating:

\[ g^{*}(y_i) = \operatorname*{arg\,max}_{g_j \in G} \bar{R}(y_i, g_j) \]

Hence, the final result can be written as:

\[ T = \{ (y_i, g^{*}(y_i), \bar{R}(y_i, g^{*}(y_i))) \mid y_i \in Y \} \]

Top 5 Highest Rated Shows Per Year

To better understand yearly trends in television ratings, we extracted the top five highest-rated shows for each year based on their average IMDb ratings.
The dataset was first grouped by year, title, and genre to calculate the mean rating (av_rating) for each show.
If a show appeared multiple times within the same year, its average rating was taken to ensure consistency.
From there, only the top five shows per year were selected, giving a clear picture of which series performed best annually.

The results are visualized in Figure 2, which presents a bar chart of the top five shows for every year.
Each bar represents one show, with colors corresponding to different years.
The chart provides insight into how audience preferences evolved over time and which titles stood out in their respective years.
[You can add an observation here, e.g., “Comedy and drama shows consistently appear in the top five, while newer genres gain visibility in later years.”]
This visualization makes it easy to identify standout titles and compare shifts in popularity across the years.

Conclusion!


By looking at the populatiry of shows and genres over time, we can see the how society’s taste in shows changes over time. Analysis of television ratings from 1990 to 2018 reveals a clear evolution in audience preferences and genre popularity. Early 1990s audiences favored family-centered dramas and comedies, exemplified by Parenthood and Are You Afraid of the Dark?, both achieving exceptionally high ratings. Over time, viewer tastes shifted toward complex, morally ambiguous narratives, with critically acclaimed titles such as Breaking Bad (2013) and Game of Thrones (2016) dominating later years.

When evaluated across the entire dataset, the highest-rated genres were War, Sport, History, Fantasy, and Music, suggesting consistent appreciation for storytelling grounded in realism, human struggle, and historical or imaginative depth. The steadily rising ratings of Fantasy and Drama genres reflect growing cultural interest in escapism and intricate world-building, paralleling the rise of premium cable and streaming platforms that enabled higher production quality and serialized storytelling.

Overall, the data indicate that societal taste has evolved from valuing light-hearted or family-oriented narratives toward favoring intense, cinematic, and thematically rich genres. This trend mirrors broader social shifts—such as increased political awareness, technological optimism, and the desire for emotionally and intellectually engaging entertainment.